library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)
library(sf)
library(tmap)
library(mapview)
library(naniar)
us_renew <- read_csv(here::here("data", "renewables_cons_prod.csv")) %>%
clean_names()
renew_clean <- us_renew %>%
mutate(description = str_to_lower(description)) %>%
filter(str_detect(description, pattern = "consumption")) %>%
filter(!str_detect(description, pattern = "total"))
yyyymm to daterenew_date <- renew_clean %>%
mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>%
mutate(month_sep = yearmonth(yr_mo_day)) %>%
mutate(value = as.numeric(value)) %>%
drop_na(month_sep, value)
# Make a version where month and year are separate columns:
renew_parsed <- renew_date %>%
mutate(year = year(yr_mo_day)) %>%
mutate(month = month(yr_mo_day, label = TRUE))
renew_gg <- ggplot(data = renew_date, aes(x = month_sep,
y = value,
group = description)) +
geom_line(aes(color = description))
renew_gg
Update colors with paletteer colors:
renew_gg +
scale_color_paletteer_d("palettetown::tyranitar")
key = main variable you’re looking at (not required) indes = tsibble compatible time series
renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)
Let’s look at our ts data in a few different ways:
# Autoplot knows which value is your key!
renew_ts %>% autoplot(value)
# gg_subseries breaks up years and months for you
renew_ts %>% gg_subseries(value)
# We can also explore a season plot: within each season, how have things shifted over time
# Whoop. This shit don't work.
## renew_ts %>% gg_season(value, n = year))
# But, we can make this with ggplot!!!
ggplot(data = renew_parsed, aes(x = month, y = value, group = year)) +
geom_line(aes(color = year)) +
facet_wrap(~description,
ncol = 1,
scales = "free",
strip.position = "right")
hydro_ts <- renew_ts %>%
filter(description == "hydroelectric power consumption")
hydro_ts %>% autoplot(value)
hydro_ts %>% gg_subseries(value)
ggplot(data = hydro_ts, aes(x = month, y = value, group = year)) +
geom_line(aes(color = year))
We can use a function index_by() in the tsibble package
hydro_quarterly <- hydro_ts %>%
index_by(year_qu = ~(yearquarter(.))) %>%
summarize(avg_consumption = mean(value))
STL decomposes by Loess smoothing
dcmp <- hydro_ts %>%
model(STL(value ~ season(window = 5)))
components(dcmp) %>% autoplot()
# Viewing residuals is easy too
hist(components(dcmp)$remainder)
Now, lets look at the autocorrelation
hydro_ts %>%
ACF(value) %>%
autoplot()
hydro_model <- hydro_ts %>%
model(
ARIMA(value)
) %>%
fabletools::forecast(h = "4 years")
hydro_model %>% autoplot(filter(hydro_ts, year(month_sep) > 2010))
mapview is awesome for a quick and dirty look at spatial data
world <- read_sf(here::here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
layer = "TM_WORLD_BORDERS_SIMPL-0.3")
mapview(world)